• Journal of information and communication convergence engineering
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• 제11권3호
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• pp.207-215
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• 2013

In this paper, we address the challenging computer vision problem of obtaining a reliable facial expression analysis from a naturally interacting person. We propose a system that combines a 3D generic face model, 3D head tracking, and 2D tracker to track facial landmarks and recognize expressions. First, we extract facial landmarks from a neutral frontal face, and then we deform a 3D generic face to fit the input face. Next, we use our real-time 3D head tracking module to track a person's head in 3D and predict facial landmark positions in 2D using the projection from the updated 3D face model. Finally, we use tracked 2D landmarks to update the 3D landmarks. This integrated tracking loop enables efficient tracking of the non-rigid parts of a face in the presence of large 3D head motion. We conducted experiments for facial expression recognition using both framebased and sequence-based approaches. Our method provides a 75.9% recognition rate in 8 subjects with 7 key expressions. Our approach provides a considerable step forward toward new applications including human-computer interactions, behavioral science, robotics, and game applications.

• 한국자동차공학회논문집
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• 제8권3호
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• pp.139-150
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• 2000

Although a number of neck injuries are generated, the data which quantify the kinematic response of the human head and cervical spine in low-speed rear-end automobile collisions is very limited. On this problem, just few in vitro experimental research or some experimental research using dummy on neck injury by rear-end collision was conducted, thus systematic research is requested on full scale injury mechanism. An occupant model for the response of the occupant subject to rear-end collision using commercial dynamics package DADS was developed. Developed model shows more close agreement with the experimental data compared with the MADYMO simulation results for the cases of ${\delta}V=16$ kph in sled test. For the case of ${\delta}V=8$ kph and 33.5 kph with production seat, model also shows its reliable response compared with experimental results using Hybrid III and Hybird III with RID.

• 해양환경안전학회지
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• 제6권1호
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• pp.1-9
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• 2000

The paper describes system design of next-generation Ship Simulator using Virtual Reality (VRSS), well known as human-computer interaction. VRSS system is required to have special condition that comprises multiple user participants such as captain, officer, pilot, and quartermaster. To cope with that condition, core technologies were explored and proposed multi-networking system with broker server. The evaluation of the proposed system was done with PC-based immersion-type VR device, constituted with HMD (Head Mounted Display), Head Tracking Sensor, Puck, Headphone, and Microphone. Using the VR device, assessment test was carried out in a virtual bridge with 3D objects, which are created by VRML (Virtual Reality Model Language) program. As results of tests, it is shown that the cybernetic 3D objects were act as if real things in a real ship's bridge. Therefore, interesting interaction with participants can be obtained in the system, Thus, we found that the proposed system architecture can be applicable to VRSS system construction.

• 스마트미디어저널
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• 제7권4호
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• pp.61-69
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• 2018

Multi-tracking of general objects and specific faces is an important topic in the field of computer vision applicable to many branches of industry such as biometrics, security, etc. The rapid development of deep neural networks has resulted in a dramatic improvement in face recognition and object detection problems, which helps improve the multiple-face tracking techniques exploiting the tracking-by-detection method. Our proposed method uses face detection trained with a head dataset to resolve the face deformation problem in the tracking process. Further, we use robust face features extracted from the deep face recognition network to match the tracklets with tracking faces using Hungarian matching method. We achieved promising results regarding the usage of deep face features and head detection in a face tracking benchmark.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제7권4호
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• pp.256-261
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• 2007

Electroencephalographic(EEG) is used to record activities of human brain in the area of psychology for many years. As technology developed, neural basis of functional areas of emotion processing is revealed gradually. So we measure fundamental areas of human brain that controls emotion of human by using EEG. Hands gestures such as shaking and head gesture such as nodding are often used as human body languages for communication with each other, and their recognition is important that it is a useful communication medium between human and computers. Research methods about gesture recognition are used of computer vision. Many researchers study Emotion Recognition method which uses one of EEG signals and Gestures in the existing research. In this paper, we use together EEG signals and Gestures for Emotion Recognition of human. And we select the driver emotion as a specific target. The experimental result shows that using of both EEG signals and gestures gets high recognition rates better than using EEG signals or gestures. Both EEG signals and gestures use Interactive Feature Selection(IFS) for the feature selection whose method is based on the reinforcement learning.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 하계학술대회 논문집 D
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• pp.3037-3040
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• 2000

Real-Time object tracking has emerged as an important component in several application areas including machine vision. surveillance. Human-Computer Interaction. image-based control. and so on. And there has been developed various algorithms for a long time. But in many cases. they have showed limited results under uncontrolled situation such as illumination changes or cluttered background. In this paper. we present a novel. computationally efficient algorithm for tracking human face robustly under illumination changes and cluttered backgrounds. Previous algorithms usually defines color model as a 2D membership function in a color space without consideration for illumination changes. Our new algorithm developed here. however. constructs a 3D color model by analysing plenty of images acquired under various illumination conditions. The algorithm described is applied to a mobile head-eye robot and experimented under various uncontrolled environments. It can track an human face more than 100 frames per second excluding image acquisition time.

• 대한전기학회논문지:전기물성ㆍ응용부문C
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• 제51권10호
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• pp.485-491
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• 2002

Since Wertheimer and Leeper reported possible adverse health effects of magnetic field in 1979, worldwide researches on this issue have been conducted. More recently, the U.S. Congress instructed the NIEHS (National Institute of Environmental Health Sciences), NIH (National Institute of Health) and DOE (Department of Energy) to direct and manage EMF RAPID (Electric and Magnetic Fields Research and Public Information Dissemination) program aimed at providing scientific evidence to clarify the potential for health risks from exposure to extremely low frequency electric and magnetic fields(ELF-EMF). Although they concluded that the scientific evidence suggesting adverse health risks of ELF-EMF is weak, the exposure to ELF-EMF cannot be recognized as entirely safe. Therefore, the purpose of this article is to describe magnetic field 3-D calculation and to evluate eddy current of human body compare to international guide line recognized one of the basic problems. In open boundary problem, Magnetic field using FEM is not advantageous in the point of the division of area and the proposition of the fictitious boundary. Therefore, we induced the analytic equation of magnetic field calculations so but the finite line segment based on Biot-Savarts law Also, Eddy currents induced due to ELF-EMF magnetic field are computed. To calculate induced currents, impedance method is used in this paper, An example model of human head with resolution of 1.27cm is used. In this paper, We evaluate the magnetic field and eddy current of human head around 765 kV transmission lines compare to international guide line.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.1202-1205
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• 2005

This paper describes a method for vision-based person identification that can detect, track, and recognize person from video using multiple cues: height and dressing colors. The method does not require constrained target's pose or fully frontal face image to identify the person. First, the system, which is connected to a pan-tilt-zoom camera, detects target using motion detection and human cardboard model. The system keeps tracking the moving target while it is trying to identify whether it is a human and identify who it is among the registered persons in the database. To segment the moving target from the background scene, we employ a version of background subtraction technique and some spatial filtering. Once the target is segmented, we then align the target with the generic human cardboard model to verify whether the detected target is a human. If the target is identified as a human, the card board model is also used to segment the body parts to obtain some salient features such as head, torso, and legs. The whole body silhouette is also analyzed to obtain the target's shape information such as height and slimness. We then use these multiple cues (at present, we uses shirt color, trousers color, and body height) to recognize the target using a supervised self-organization process. We preliminary tested the system on a set of 5 subjects with multiple clothes. The recognition rate is 100% if the person is wearing the clothes that were learned before. In case a person wears new dresses the system fail to identify. This means height is not enough to classify persons. We plan to extend the work by adding more cues such as skin color, and face recognition by utilizing the zoom capability of the camera to obtain high resolution view of face; then, evaluate the system with more subjects.

• 한국정밀공학회지
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• 제25권5호
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• pp.155-163
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• 2008

This study investigated biomechanical response through the 3-dimensional virtual skeletal model developed and validated. Ten male subjects in standing posture were exposed to whole body vibrations and measured acceleration on anatomical of interest (head, $7^{th}$ cervical, $10^{th}$ thoracic, $4^{th}$ lumbar, knee joint and bottom of the vibrator). Three dimensional virtual skeletal model and vibration machine were created by using BRG LifeMOD and MSC.ADAMS. The results of forward dynamic analysis were compared with results of experiment. The results showed that the accuracy of developed model was $73.2{\pm}19.2%$ for all conditions.

• 제어로봇시스템학회논문지
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• 제20권8호
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• pp.875-881
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• 2014

The redundancy resolution of the seven DOF (Degree of Freedom) upper limb exoskeleton is key to the synchronous motion between a robot and a human user. According to the seven DOF human arm model, positioning and orientating the wrist can be completed by multiple arm configurations that results in the non-unique solution to the inverse kinematics. This paper presents analysis on the kinematic and dynamic aspect of the human arm movement and its effect on the redundancy resolution of the seven DOF human arm model. The redundancy of the arm is expressed mathematically by defining the swivel angle. The final form of swivel angle can be represented as a linear combination of two different swivel angles achieved by optimizing two cost functions based on kinematic and dynamic criteria. The kinematic criterion is to maximize the projection of the longest principal axis of the manipulability ellipsoid of the human arm on the vector connecting the wrist and the virtual target on the head region. The dynamic criterion is to minimize the mechanical work done in the joint space for each of two consecutive points along the task space trajectory. The contribution of each criterion on the redundancy was verified by the post processing of experimental data collected with a motion capture system. Results indicate that the bimodal redundancy resolution approach improved the accuracy of the predicted swivel angle. Statistical testing of the dynamic constraint contribution shows that under moderate speeds and no load, the dynamic component of the human arm is not dominant, and it is enough to resolve the redundancy without dynamic constraint for the realtime application.